Overview

Dataset statistics

Number of variables21
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory170.6 B

Variable types

Numeric19
DateTime1
Categorical1

Alerts

df_index is highly correlated with CaloriesBurned_R7DM and 5 other fieldsHigh correlation
CaloriesBurned is highly correlated with Steps and 6 other fieldsHigh correlation
Steps is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
Distance is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
Floors is highly correlated with CaloriesBurned and 3 other fieldsHigh correlation
SedentaryMinutes is highly correlated with CaloriesBurned and 2 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with CaloriesBurned and 2 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with VeryActiveMinutesHigh correlation
VeryActiveMinutes is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
ActivityCalories is highly correlated with CaloriesBurned and 6 other fieldsHigh correlation
CaloriesBurned_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Steps_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Distance_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Floors_R7DM is highly correlated with VeryActiveMinutes_R7DMHigh correlation
SedentaryMinutes_R7DM is highly correlated with LightlyActiveMinutes_R7DMHigh correlation
LightlyActiveMinutes_R7DM is highly correlated with SedentaryMinutes_R7DMHigh correlation
FairlyActiveMinutes_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
VeryActiveMinutes_R7DM is highly correlated with df_index and 6 other fieldsHigh correlation
ActivityCalories_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with CaloriesBurned_R7DM and 5 other fieldsHigh correlation
CaloriesBurned is highly correlated with Steps and 5 other fieldsHigh correlation
Steps is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
Distance is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
Floors is highly correlated with Steps and 1 other fieldsHigh correlation
SedentaryMinutes is highly correlated with LightlyActiveMinutesHigh correlation
LightlyActiveMinutes is highly correlated with CaloriesBurned and 2 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with CaloriesBurned and 2 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with CaloriesBurned and 4 other fieldsHigh correlation
ActivityCalories is highly correlated with CaloriesBurned and 5 other fieldsHigh correlation
CaloriesBurned_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Steps_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Distance_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
Floors_R7DM is highly correlated with VeryActiveMinutes_R7DMHigh correlation
SedentaryMinutes_R7DM is highly correlated with LightlyActiveMinutes_R7DMHigh correlation
LightlyActiveMinutes_R7DM is highly correlated with SedentaryMinutes_R7DM and 1 other fieldsHigh correlation
FairlyActiveMinutes_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
VeryActiveMinutes_R7DM is highly correlated with df_index and 7 other fieldsHigh correlation
ActivityCalories_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with VeryActiveMinutes_R7DMHigh correlation
CaloriesBurned is highly correlated with Steps and 3 other fieldsHigh correlation
Steps is highly correlated with CaloriesBurned and 3 other fieldsHigh correlation
Distance is highly correlated with CaloriesBurned and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with CaloriesBurned and 3 other fieldsHigh correlation
ActivityCalories is highly correlated with CaloriesBurned and 3 other fieldsHigh correlation
CaloriesBurned_R7DM is highly correlated with Steps_R7DM and 4 other fieldsHigh correlation
Steps_R7DM is highly correlated with CaloriesBurned_R7DM and 3 other fieldsHigh correlation
Distance_R7DM is highly correlated with CaloriesBurned_R7DM and 3 other fieldsHigh correlation
SedentaryMinutes_R7DM is highly correlated with LightlyActiveMinutes_R7DMHigh correlation
LightlyActiveMinutes_R7DM is highly correlated with SedentaryMinutes_R7DMHigh correlation
FairlyActiveMinutes_R7DM is highly correlated with CaloriesBurned_R7DM and 1 other fieldsHigh correlation
VeryActiveMinutes_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
ActivityCalories_R7DM is highly correlated with CaloriesBurned_R7DM and 3 other fieldsHigh correlation
df_index is highly correlated with Date and 8 other fieldsHigh correlation
Date is highly correlated with df_index and 19 other fieldsHigh correlation
CaloriesBurned is highly correlated with Date and 7 other fieldsHigh correlation
Steps is highly correlated with Date and 10 other fieldsHigh correlation
Distance is highly correlated with Date and 8 other fieldsHigh correlation
Floors is highly correlated with Date and 4 other fieldsHigh correlation
SedentaryMinutes is highly correlated with Date and 7 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with Date and 3 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with Date and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with Date and 4 other fieldsHigh correlation
ActivityCalories is highly correlated with Date and 5 other fieldsHigh correlation
Day is highly correlated with DateHigh correlation
CaloriesBurned_R7DM is highly correlated with df_index and 12 other fieldsHigh correlation
Steps_R7DM is highly correlated with Date and 7 other fieldsHigh correlation
Distance_R7DM is highly correlated with df_index and 10 other fieldsHigh correlation
Floors_R7DM is highly correlated with df_index and 6 other fieldsHigh correlation
SedentaryMinutes_R7DM is highly correlated with df_index and 5 other fieldsHigh correlation
LightlyActiveMinutes_R7DM is highly correlated with df_index and 6 other fieldsHigh correlation
FairlyActiveMinutes_R7DM is highly correlated with df_index and 8 other fieldsHigh correlation
VeryActiveMinutes_R7DM is highly correlated with df_index and 9 other fieldsHigh correlation
ActivityCalories_R7DM is highly correlated with df_index and 10 other fieldsHigh correlation
Day is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique
Steps has unique values Unique
SedentaryMinutes has unique values Unique
ActivityCalories has unique values Unique
CaloriesBurned_R7DM has unique values Unique
Steps_R7DM has unique values Unique
Distance_R7DM has unique values Unique
SedentaryMinutes_R7DM has unique values Unique
ActivityCalories_R7DM has unique values Unique
df_index has 1 (2.0%) zeros Zeros
VeryActiveMinutes has 1 (2.0%) zeros Zeros

Reproduction

Analysis started2022-12-02 17:02:31.206279
Analysis finished2022-12-02 17:03:32.429729
Duration1 minute and 1.22 second
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE
ZEROS

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9
Minimum0
Maximum55
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:32.580153image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q118.25
median30.5
Q342.75
95-th percentile52.55
Maximum55
Range55
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation15.59990842
Coefficient of variation (CV)0.5217360677
Kurtosis-0.9255101363
Mean29.9
Median Absolute Deviation (MAD)12.5
Skewness-0.2099761711
Sum1495
Variance243.3571429
MonotonicityStrictly increasing
2022-12-02T17:03:32.769537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
2.0%
431
 
2.0%
331
 
2.0%
341
 
2.0%
351
 
2.0%
361
 
2.0%
371
 
2.0%
381
 
2.0%
391
 
2.0%
401
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
01
2.0%
11
2.0%
21
2.0%
31
2.0%
41
2.0%
111
2.0%
121
2.0%
131
2.0%
141
2.0%
151
2.0%
ValueCountFrequency (%)
551
2.0%
541
2.0%
531
2.0%
521
2.0%
511
2.0%
501
2.0%
491
2.0%
481
2.0%
471
2.0%
461
2.0%

Date
Date

HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-25 00:00:00
2022-12-02T17:03:32.975682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-02T17:03:33.180174image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CaloriesBurned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3220.22
Minimum2403
Maximum4174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:33.385132image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2403
5-th percentile2635.5
Q12893
median3160
Q33460
95-th percentile3939.15
Maximum4174
Range1771
Interquartile range (IQR)567

Descriptive statistics

Standard deviation395.4249737
Coefficient of variation (CV)0.1227943972
Kurtosis-0.3291045775
Mean3220.22
Median Absolute Deviation (MAD)274
Skewness0.2959691934
Sum161011
Variance156360.9098
MonotonicityNot monotonic
2022-12-02T17:03:33.583181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
33542
 
4.0%
30681
 
2.0%
28871
 
2.0%
30771
 
2.0%
40081
 
2.0%
35461
 
2.0%
36481
 
2.0%
35961
 
2.0%
34701
 
2.0%
29561
 
2.0%
Other values (39)39
78.0%
ValueCountFrequency (%)
24031
2.0%
26281
2.0%
26311
2.0%
26411
2.0%
27451
2.0%
27691
2.0%
28131
2.0%
28151
2.0%
28311
2.0%
28601
2.0%
ValueCountFrequency (%)
41741
2.0%
40081
2.0%
39901
2.0%
38771
2.0%
37451
2.0%
36481
2.0%
36231
2.0%
36151
2.0%
36001
2.0%
35961
2.0%

Steps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13740.86
Minimum6239
Maximum24424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:33.801278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6239
5-th percentile7872
Q110003.25
median13578.5
Q316552.75
95-th percentile21660
Maximum24424
Range18185
Interquartile range (IQR)6549.5

Descriptive statistics

Standard deviation4372.176052
Coefficient of variation (CV)0.3181879483
Kurtosis-0.3408306054
Mean13740.86
Median Absolute Deviation (MAD)3239.5
Skewness0.4621262158
Sum687043
Variance19115923.43
MonotonicityNot monotonic
2022-12-02T17:03:34.002278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99441
 
2.0%
124061
 
2.0%
126961
 
2.0%
155191
 
2.0%
170891
 
2.0%
136271
 
2.0%
157331
 
2.0%
175071
 
2.0%
166601
 
2.0%
105631
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
62391
2.0%
71931
2.0%
78001
2.0%
79601
2.0%
84211
2.0%
85331
2.0%
86701
2.0%
89301
2.0%
96121
2.0%
98081
2.0%
ValueCountFrequency (%)
244241
2.0%
232881
2.0%
225241
2.0%
206041
2.0%
195611
2.0%
190391
2.0%
190171
2.0%
178811
2.0%
177351
2.0%
175071
2.0%

Distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.1448
Minimum4.73
Maximum17.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:34.218050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4.73
5-th percentile5.8115
Q17.4125
median10.18
Q312.3025
95-th percentile16.0675
Maximum17.92
Range13.19
Interquartile range (IQR)4.89

Descriptive statistics

Standard deviation3.228241601
Coefficient of variation (CV)0.3182163869
Kurtosis-0.4260651855
Mean10.1448
Median Absolute Deviation (MAD)2.44
Skewness0.4342777611
Sum507.24
Variance10.42154384
MonotonicityNot monotonic
2022-12-02T17:03:34.446340image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
11.632
 
4.0%
7.251
 
2.0%
9.661
 
2.0%
12.591
 
2.0%
10.351
 
2.0%
11.651
 
2.0%
12.991
 
2.0%
12.331
 
2.0%
7.711
 
2.0%
6.261
 
2.0%
Other values (39)39
78.0%
ValueCountFrequency (%)
4.731
2.0%
5.251
2.0%
5.691
2.0%
5.961
2.0%
6.191
2.0%
6.261
2.0%
6.391
2.0%
6.561
2.0%
7.021
2.0%
7.171
2.0%
ValueCountFrequency (%)
17.921
2.0%
16.971
2.0%
16.631
2.0%
15.381
2.0%
14.421
2.0%
14.281
2.0%
13.981
2.0%
13.051
2.0%
12.991
2.0%
12.931
2.0%

Floors
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.84
Minimum4
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:34.662357image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.45
Q115.25
median18.5
Q322.75
95-th percentile36.85
Maximum47
Range43
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation8.706928979
Coefficient of variation (CV)0.4388573074
Kurtosis1.61444924
Mean19.84
Median Absolute Deviation (MAD)4
Skewness1.002680167
Sum992
Variance75.81061224
MonotonicityNot monotonic
2022-12-02T17:03:34.836044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
177
 
14.0%
216
 
12.0%
163
 
6.0%
103
 
6.0%
123
 
6.0%
192
 
4.0%
182
 
4.0%
282
 
4.0%
142
 
4.0%
252
 
4.0%
Other values (16)18
36.0%
ValueCountFrequency (%)
41
 
2.0%
61
 
2.0%
81
 
2.0%
91
 
2.0%
103
6.0%
123
6.0%
142
 
4.0%
151
 
2.0%
163
6.0%
177
14.0%
ValueCountFrequency (%)
471
2.0%
421
2.0%
401
2.0%
331
2.0%
311
2.0%
301
2.0%
282
4.0%
271
2.0%
261
2.0%
252
4.0%

SedentaryMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700.3
Minimum389
Maximum1260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:35.008339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile438.15
Q1569.5
median655
Q3785.25
95-th percentile1109.7
Maximum1260
Range871
Interquartile range (IQR)215.75

Descriptive statistics

Standard deviation201.4192754
Coefficient of variation (CV)0.2876185569
Kurtosis0.9550079998
Mean700.3
Median Absolute Deviation (MAD)96
Skewness1.110680279
Sum35015
Variance40569.72449
MonotonicityNot monotonic
2022-12-02T17:03:35.201536image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4641
 
2.0%
5601
 
2.0%
6641
 
2.0%
5671
 
2.0%
4931
 
2.0%
5831
 
2.0%
11021
 
2.0%
10651
 
2.0%
6701
 
2.0%
7321
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
3891
2.0%
4041
2.0%
4171
2.0%
4641
2.0%
4931
2.0%
5231
2.0%
5331
2.0%
5341
2.0%
5481
2.0%
5581
2.0%
ValueCountFrequency (%)
12601
2.0%
11981
2.0%
11161
2.0%
11021
2.0%
11011
2.0%
10651
2.0%
8781
2.0%
8711
2.0%
8411
2.0%
8371
2.0%

LightlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.44
Minimum76
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:35.408509image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile145.1
Q1214.75
median268.5
Q3311
95-th percentile379
Maximum390
Range314
Interquartile range (IQR)96.25

Descriptive statistics

Standard deviation74.84764389
Coefficient of variation (CV)0.2851990698
Kurtosis-0.2586911485
Mean262.44
Median Absolute Deviation (MAD)43.5
Skewness-0.2871448467
Sum13122
Variance5602.169796
MonotonicityNot monotonic
2022-12-02T17:03:35.599439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2093
 
6.0%
3792
 
4.0%
3012
 
4.0%
2462
 
4.0%
2772
 
4.0%
3122
 
4.0%
1961
 
2.0%
3281
 
2.0%
1671
 
2.0%
2741
 
2.0%
Other values (33)33
66.0%
ValueCountFrequency (%)
761
2.0%
1081
2.0%
1371
2.0%
1551
2.0%
1601
2.0%
1651
2.0%
1671
2.0%
1691
2.0%
1831
2.0%
1961
2.0%
ValueCountFrequency (%)
3901
2.0%
3811
2.0%
3792
4.0%
3761
2.0%
3741
2.0%
3641
2.0%
3501
2.0%
3281
2.0%
3271
2.0%
3161
2.0%

FairlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.66
Minimum4
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:35.792515image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.9
Q114.25
median27.5
Q339.5
95-th percentile54.05
Maximum81
Range77
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation16.91227063
Coefficient of variation (CV)0.6114342239
Kurtosis0.6935601359
Mean27.66
Median Absolute Deviation (MAD)13
Skewness0.766996281
Sum1383
Variance286.024898
MonotonicityNot monotonic
2022-12-02T17:03:35.962422image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
43
 
6.0%
283
 
6.0%
183
 
6.0%
333
 
6.0%
112
 
4.0%
82
 
4.0%
482
 
4.0%
142
 
4.0%
432
 
4.0%
382
 
4.0%
Other values (23)26
52.0%
ValueCountFrequency (%)
43
6.0%
61
 
2.0%
82
4.0%
92
4.0%
101
 
2.0%
112
4.0%
142
4.0%
151
 
2.0%
161
 
2.0%
171
 
2.0%
ValueCountFrequency (%)
811
2.0%
641
2.0%
591
2.0%
482
4.0%
461
2.0%
451
2.0%
441
2.0%
432
4.0%
422
4.0%
401
2.0%

VeryActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.04
Minimum0
Maximum129
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:36.157430image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.45
Q115.75
median38
Q378.5
95-th percentile104
Maximum129
Range129
Interquartile range (IQR)62.75

Descriptive statistics

Standard deviation34.37723499
Coefficient of variation (CV)0.730808567
Kurtosis-0.8779098613
Mean47.04
Median Absolute Deviation (MAD)25.5
Skewness0.597597011
Sum2352
Variance1181.794286
MonotonicityNot monotonic
2022-12-02T17:03:36.353518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
124
 
8.0%
132
 
4.0%
422
 
4.0%
1042
 
4.0%
252
 
4.0%
912
 
4.0%
332
 
4.0%
61
 
2.0%
891
 
2.0%
1291
 
2.0%
Other values (31)31
62.0%
ValueCountFrequency (%)
01
 
2.0%
61
 
2.0%
91
 
2.0%
101
 
2.0%
111
 
2.0%
124
8.0%
132
4.0%
141
 
2.0%
151
 
2.0%
181
 
2.0%
ValueCountFrequency (%)
1291
2.0%
1071
2.0%
1042
4.0%
1001
2.0%
951
2.0%
941
2.0%
912
4.0%
891
2.0%
831
2.0%
821
2.0%

ActivityCalories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1769.42
Minimum859
Maximum2939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:36.576596image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum859
5-th percentile1119.3
Q11370
median1714
Q32099.75
95-th percentile2639.25
Maximum2939
Range2080
Interquartile range (IQR)729.75

Descriptive statistics

Standard deviation475.8194689
Coefficient of variation (CV)0.268912677
Kurtosis-0.3315219192
Mean1769.42
Median Absolute Deviation (MAD)357.5
Skewness0.379279716
Sum88471
Variance226404.1669
MonotonicityNot monotonic
2022-12-02T17:03:36.767646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16601
 
2.0%
13861
 
2.0%
15481
 
2.0%
18831
 
2.0%
27091
 
2.0%
21841
 
2.0%
21931
 
2.0%
21811
 
2.0%
20251
 
2.0%
13841
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
8591
2.0%
10291
2.0%
10591
2.0%
11931
2.0%
11981
2.0%
12421
2.0%
12811
2.0%
12941
2.0%
13021
2.0%
13081
2.0%
ValueCountFrequency (%)
29391
2.0%
27611
2.0%
27091
2.0%
25541
2.0%
23841
2.0%
23221
2.0%
22171
2.0%
21931
2.0%
21841
2.0%
21811
2.0%

Day
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Wednesday
Saturday
Sunday
Monday
Tuesday
Other values (2)
14 

Length

Max length9
Median length7
Mean length7.18
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSunday
3rd rowMonday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Wednesday8
16.0%
Saturday7
14.0%
Sunday7
14.0%
Monday7
14.0%
Tuesday7
14.0%
Thursday7
14.0%
Friday7
14.0%

Length

2022-12-02T17:03:36.960518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-02T17:03:37.084602image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
wednesday8
16.0%
saturday7
14.0%
sunday7
14.0%
monday7
14.0%
tuesday7
14.0%
thursday7
14.0%
friday7
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CaloriesBurned_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3209.102476
Minimum2937.285714
Maximum3528.428571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:37.240565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2937.285714
5-th percentile2995.864286
Q13092.842857
median3170.928571
Q33308.678571
95-th percentile3492.435714
Maximum3528.428571
Range591.1428571
Interquartile range (IQR)215.8357143

Descriptive statistics

Standard deviation158.4385331
Coefficient of variation (CV)0.04937160288
Kurtosis-0.7620396478
Mean3209.102476
Median Absolute Deviation (MAD)90.21428571
Skewness0.4952398401
Sum160455.1238
Variance25102.76876
MonotonicityNot monotonic
2022-12-02T17:03:37.444628image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30681
 
2.0%
3218.4285711
 
2.0%
2977.2857141
 
2.0%
3080.5714291
 
2.0%
32611
 
2.0%
3288.4285711
 
2.0%
3407.4285711
 
2.0%
3469.5714291
 
2.0%
3528.4285711
 
2.0%
3511.1428571
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
2937.2857141
2.0%
2971.2857141
2.0%
2977.2857141
2.0%
3018.5714291
2.0%
3023.51
2.0%
3026.7142861
2.0%
30681
2.0%
30781
2.0%
3080.5714291
2.0%
30821
2.0%
ValueCountFrequency (%)
3528.4285711
2.0%
3523.8571431
2.0%
3511.1428571
2.0%
3469.5714291
2.0%
3452.8571431
2.0%
3440.5714291
2.0%
3430.5714291
2.0%
3419.8571431
2.0%
3411.7142861
2.0%
3407.4285711
2.0%

Steps_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13570.39381
Minimum9944
Maximum16938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:37.643627image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum9944
5-th percentile11002.275
Q112696.82143
median13769.21429
Q314733.96429
95-th percentile15642.2
Maximum16938
Range6994
Interquartile range (IQR)2037.142857

Descriptive statistics

Standard deviation1553.343161
Coefficient of variation (CV)0.114465592
Kurtosis-0.3260775531
Mean13570.39381
Median Absolute Deviation (MAD)1017.428571
Skewness-0.3555418373
Sum678519.6905
Variance2412874.977
MonotonicityNot monotonic
2022-12-02T17:03:37.872998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99441
 
2.0%
13920.285711
 
2.0%
11985.857141
 
2.0%
12786.285711
 
2.0%
13854.428571
 
2.0%
13081.285711
 
2.0%
14090.285711
 
2.0%
14304.428571
 
2.0%
15547.285711
 
2.0%
15242.571431
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
99441
2.0%
10434.51
2.0%
10846.51
2.0%
11192.666671
2.0%
11258.428571
2.0%
112641
2.0%
11394.714291
2.0%
11713.857141
2.0%
11860.166671
2.0%
11985.857141
2.0%
ValueCountFrequency (%)
169381
2.0%
159461
2.0%
15719.857141
2.0%
15547.285711
2.0%
15521.714291
2.0%
15245.714291
2.0%
15242.571431
2.0%
15126.285711
2.0%
15034.428571
2.0%
14986.857141
2.0%

Distance_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01852238
Minimum7.25
Maximum12.46142857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:38.110035image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum7.25
5-th percentile8.035375
Q19.401071429
median10.17071429
Q310.87392857
95-th percentile11.58521429
Maximum12.46142857
Range5.211428571
Interquartile range (IQR)1.472857143

Descriptive statistics

Standard deviation1.16794259
Coefficient of variation (CV)0.1165783281
Kurtosis-0.3094349277
Mean10.01852238
Median Absolute Deviation (MAD)0.755
Skewness-0.4211387988
Sum500.926119
Variance1.364089893
MonotonicityNot monotonic
2022-12-02T17:03:38.348109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.251
 
2.0%
10.262857141
 
2.0%
8.8757142861
 
2.0%
9.5028571431
 
2.0%
10.298571431
 
2.0%
9.781
 
2.0%
10.531428571
 
2.0%
10.691
 
2.0%
11.61
 
2.0%
11.321428571
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
7.251
2.0%
7.631
2.0%
7.92251
2.0%
8.1733333331
2.0%
8.2261
2.0%
8.3585714291
2.0%
8.4328571431
2.0%
8.5742857141
2.0%
8.661
2.0%
8.821
2.0%
ValueCountFrequency (%)
12.461428571
2.0%
11.757142861
2.0%
11.61
2.0%
11.567142861
2.0%
11.517142861
2.0%
11.321428571
2.0%
11.214285711
2.0%
11.112857141
2.0%
11.031428571
2.0%
11.024285711
2.0%

Floors_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.61890476
Minimum15.42857143
Maximum25.57142857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:38.542548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum15.42857143
5-th percentile15.84285714
Q117.44642857
median19.57142857
Q320.71428571
95-th percentile23.66428571
Maximum25.57142857
Range10.14285714
Interquartile range (IQR)3.267857143

Descriptive statistics

Standard deviation2.602890991
Coefficient of variation (CV)0.1326725943
Kurtosis-0.5691613975
Mean19.61890476
Median Absolute Deviation (MAD)1.892857143
Skewness0.3333491354
Sum980.9452381
Variance6.775041513
MonotonicityNot monotonic
2022-12-02T17:03:38.722517image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
205
 
10.0%
18.857142863
 
6.0%
19.571428573
 
6.0%
23.142857143
 
6.0%
20.428571432
 
4.0%
20.714285712
 
4.0%
192
 
4.0%
16.428571432
 
4.0%
171
 
2.0%
17.857142861
 
2.0%
Other values (26)26
52.0%
ValueCountFrequency (%)
15.428571431
2.0%
15.571428571
2.0%
15.714285711
2.0%
161
2.0%
16.142857141
2.0%
16.428571432
4.0%
16.61
2.0%
16.666666671
2.0%
16.751
2.0%
171
2.0%
ValueCountFrequency (%)
25.571428571
 
2.0%
251
 
2.0%
23.857142861
 
2.0%
23.428571431
 
2.0%
23.285714291
 
2.0%
23.142857143
6.0%
231
 
2.0%
22.428571431
 
2.0%
22.285714291
 
2.0%
21.285714291
 
2.0%

SedentaryMinutes_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean693.0114286
Minimum464
Maximum897.7142857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:38.927481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum464
5-th percentile547.2833333
Q1615.9285714
median698.5
Q3743.75
95-th percentile867.7
Maximum897.7142857
Range433.7142857
Interquartile range (IQR)127.8214286

Descriptive statistics

Standard deviation97.42001151
Coefficient of variation (CV)0.1405748989
Kurtosis-0.1746731263
Mean693.0114286
Median Absolute Deviation (MAD)74.35714286
Skewness0.1490914594
Sum34650.57143
Variance9490.658643
MonotonicityNot monotonic
2022-12-02T17:03:39.131479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4641
 
2.0%
813.71428571
 
2.0%
696.28571431
 
2.0%
679.28571431
 
2.0%
624.28571431
 
2.0%
627.85714291
 
2.0%
660.85714291
 
2.0%
722.71428571
 
2.0%
734.85714291
 
2.0%
744.57142861
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
4641
2.0%
520.51
2.0%
521.33333331
2.0%
5791
2.0%
591.42857141
2.0%
596.71428571
2.0%
597.85714291
2.0%
601.16666671
2.0%
606.71428571
2.0%
607.57142861
2.0%
ValueCountFrequency (%)
897.71428571
2.0%
889.28571431
2.0%
885.57142861
2.0%
845.85714291
2.0%
830.14285711
2.0%
8171
2.0%
814.42857141
2.0%
813.71428571
2.0%
798.42857141
2.0%
783.14285711
2.0%

LightlyActiveMinutes_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.914
Minimum218.2857143
Maximum379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:39.361616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum218.2857143
5-th percentile228.2857143
Q1242.1785714
median255.7857143
Q3292.3928571
95-th percentile327.4
Maximum379
Range160.7142857
Interquartile range (IQR)50.21428571

Descriptive statistics

Standard deviation34.66775574
Coefficient of variation (CV)0.129883617
Kurtosis1.255551545
Mean266.914
Median Absolute Deviation (MAD)20.21428571
Skewness1.091438728
Sum13345.7
Variance1201.853288
MonotonicityNot monotonic
2022-12-02T17:03:39.561204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
274.42857142
 
4.0%
228.28571432
 
4.0%
3791
 
2.0%
218.28571431
 
2.0%
232.71428571
 
2.0%
242.28571431
 
2.0%
255.42857141
 
2.0%
249.42857141
 
2.0%
243.42857141
 
2.0%
251.28571431
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
218.28571431
2.0%
222.71428571
2.0%
228.28571432
4.0%
230.42857141
2.0%
230.85714291
2.0%
232.71428571
2.0%
235.28571431
2.0%
235.85714291
2.0%
237.71428571
2.0%
238.28571431
2.0%
ValueCountFrequency (%)
3791
2.0%
353.66666671
2.0%
335.51
2.0%
317.51
2.0%
304.85714291
2.0%
303.21
2.0%
300.28571431
2.0%
299.57142861
2.0%
2991
2.0%
296.85714291
2.0%

FairlyActiveMinutes_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.63961905
Minimum4
Maximum41.28571429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:39.773677image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile16.85833333
Q122.60714286
median25.78571429
Q330.67857143
95-th percentile38.83571429
Maximum41.28571429
Range37.28571429
Interquartile range (IQR)8.071428571

Descriptive statistics

Standard deviation7.409187441
Coefficient of variation (CV)0.2781266289
Kurtosis0.8726061356
Mean26.63961905
Median Absolute Deviation (MAD)4.214285714
Skewness-0.2593363127
Sum1331.980952
Variance54.89605853
MonotonicityNot monotonic
2022-12-02T17:03:39.955771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
23.571428572
 
4.0%
29.714285712
 
4.0%
23.714285712
 
4.0%
26.714285712
 
4.0%
34.428571432
 
4.0%
301
 
2.0%
38.285714291
 
2.0%
28.571428571
 
2.0%
36.714285711
 
2.0%
341
 
2.0%
Other values (35)35
70.0%
ValueCountFrequency (%)
41
2.0%
10.51
2.0%
16.333333331
2.0%
17.51
2.0%
19.61
2.0%
19.857142861
2.0%
20.857142861
2.0%
21.142857141
2.0%
21.285714291
2.0%
21.428571431
2.0%
ValueCountFrequency (%)
41.285714291
2.0%
39.857142861
2.0%
39.285714291
2.0%
38.285714291
2.0%
37.714285711
2.0%
36.714285711
2.0%
36.142857141
2.0%
35.428571431
2.0%
34.428571432
4.0%
341
2.0%

VeryActiveMinutes_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.48104762
Minimum6
Maximum77.85714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:40.164875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15.26666667
Q135.17857143
median44.92857143
Q358.28571429
95-th percentile72.05
Maximum77.85714286
Range71.85714286
Interquartile range (IQR)23.10714286

Descriptive statistics

Standard deviation17.00037714
Coefficient of variation (CV)0.3737903595
Kurtosis-0.304070077
Mean45.48104762
Median Absolute Deviation (MAD)11.92857143
Skewness-0.2515609116
Sum2274.052381
Variance289.0128229
MonotonicityNot monotonic
2022-12-02T17:03:40.936968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
59.142857142
 
4.0%
37.428571432
 
4.0%
61
 
2.0%
61.142857141
 
2.0%
38.571428571
 
2.0%
55.142857141
 
2.0%
50.714285711
 
2.0%
64.428571431
 
2.0%
73.142857141
 
2.0%
77.857142861
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
61
2.0%
141
2.0%
14.666666671
2.0%
161
2.0%
18.61
2.0%
21.428571431
2.0%
23.51
2.0%
26.285714291
2.0%
30.285714291
2.0%
31.285714291
2.0%
ValueCountFrequency (%)
77.857142861
2.0%
77.571428571
2.0%
73.142857141
2.0%
70.714285711
2.0%
68.428571431
2.0%
651
2.0%
64.428571431
2.0%
61.142857141
2.0%
601
2.0%
59.142857142
4.0%

ActivityCalories_R7DM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1761.10281
Minimum1458.857143
Maximum2141.571429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-12-02T17:03:41.155598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1458.857143
5-th percentile1502.621429
Q11635.192857
median1721.071429
Q31863.535714
95-th percentile2066.678571
Maximum2141.571429
Range682.7142857
Interquartile range (IQR)228.3428571

Descriptive statistics

Standard deviation181.449997
Coefficient of variation (CV)0.1030320297
Kurtosis-0.7755374802
Mean1761.10281
Median Absolute Deviation (MAD)117.7142857
Skewness0.460767335
Sum88055.14048
Variance32924.10141
MonotonicityNot monotonic
2022-12-02T17:03:41.352229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16601
 
2.0%
1723.2857141
 
2.0%
1472.5714291
 
2.0%
1590.2857141
 
2.0%
1806.8571431
 
2.0%
1846.7142861
 
2.0%
19741
 
2.0%
2037.2857141
 
2.0%
2103.2857141
 
2.0%
2079.8571431
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
1458.8571431
2.0%
1472.5714291
2.0%
1483.1428571
2.0%
1526.4285711
2.0%
1537.4285711
2.0%
15821
2.0%
1590.2857141
2.0%
1600.8571431
2.0%
1601.8571431
2.0%
1619.7142861
2.0%
ValueCountFrequency (%)
2141.5714291
2.0%
2103.2857141
2.0%
2079.8571431
2.0%
2050.5714291
2.0%
2044.2857141
2.0%
2037.2857141
2.0%
2020.1428571
2.0%
20201
2.0%
1995.7142861
2.0%
19741
2.0%

Interactions

2022-12-02T17:03:28.551670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-02T17:03:42.311233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-02T17:03:42.698342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-02T17:03:31.576159image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-02T17:03:32.222229image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexDateCaloriesBurnedStepsDistanceFloorsSedentaryMinutesLightlyActiveMinutesFairlyActiveMinutesVeryActiveMinutesActivityCaloriesDayCaloriesBurned_R7DMSteps_R7DMDistance_R7DMFloors_R7DMSedentaryMinutes_R7DMLightlyActiveMinutes_R7DMFairlyActiveMinutes_R7DMVeryActiveMinutes_R7DMActivityCalories_R7DM
002022-10-01306899447.2519464379461660Saturday3068.0000009944.0000007.25000019.000000464.000000379.0000004.0000006.0000001660.000000
112022-10-022979109258.011957729217261504Sunday3023.50000010434.5000007.63000019.000000520.500000335.50000010.50000016.0000001582.000000
222022-10-033496127099.261252339028122143Monday3181.00000011192.6666678.17333316.666667521.333333353.66666716.33333314.6666671769.000000
332022-10-04276998087.171775220921121242Tuesday3078.00000010846.5000007.92250016.750000579.000000317.50000017.50000014.0000001637.250000
442022-10-053146129349.441672924628371632Wednesday3091.60000011264.0000008.22600016.600000609.000000303.20000019.60000018.6000001636.200000
5112022-10-1233591484110.832256226542481934Wednesday3136.16666711860.1666678.66000017.500000601.166667296.83333323.33333323.5000001685.833333
6122022-10-132885108368.0610646273991357Thursday3100.28571411713.8571438.57428616.428571607.571429293.42857121.28571421.4285711638.857143
7132022-10-143159123008.9717111624638401690Friday3113.28571412050.4285718.82000016.142857700.714286274.42857126.14285726.2857141643.142857
8142022-10-1536152442417.9247597291451042322Saturday3204.14285713978.85714310.23571420.142857703.571429274.28571430.14285737.4285711760.000000
9152022-10-16264162394.73878716911251029Sunday3082.00000013054.5714299.58857119.571429741.285714242.71428627.71428639.2857141600.857143

Last rows

df_indexDateCaloriesBurnedStepsDistanceFloorsSedentaryMinutesLightlyActiveMinutesFairlyActiveMinutesVeryActiveMinutesActivityCaloriesDayCaloriesBurned_R7DMSteps_R7DMDistance_R7DMFloors_R7DMSedentaryMinutes_R7DMLightlyActiveMinutes_R7DMFairlyActiveMinutes_R7DMVeryActiveMinutes_R7DMActivityCalories_R7DM
40462022-11-16287284216.1948147633801324Wednesday3123.14285712667.0000009.36714320.000000677.142857230.85714323.42857150.5714291634.857143
41472022-11-1734081655512.392162530433592004Thursday3187.71428613523.00000010.03571417.000000661.857143238.28571425.42857157.2857141723.428571
42482022-11-1841742252416.632338936481912939Friday3375.42857115521.71428611.51714317.857143597.857143257.00000035.42857168.4285711958.428571
43492022-11-19283189306.56108412774121308Saturday3335.28571414506.00000010.77428617.285714613.714286274.42857130.00000057.4285711916.714286
44502022-11-2034221446710.611040438114502109Sunday3411.71428614800.42857110.98428617.428571591.428571299.00000029.71428658.5714292020.000000
45512022-11-2138771773512.932167727748952554Monday3430.57142914617.28571410.79142916.428571612.000000294.00000034.42857159.1428572044.285714
46522022-11-2233551353010.132060828144331903Tuesday3419.85714314594.57142910.77714315.571429622.571429280.00000036.71428660.0000002020.142857
47532022-11-2336001788113.054063325040742174Wednesday3523.85714315946.00000011.75714320.714286596.714286304.85714337.71428659.1428572141.571429
48542022-11-242911108177.881669527210101367Thursday3452.85714315126.28571411.11285720.000000606.714286300.28571434.42857152.1428572050.571429
49552022-11-253083111778.161766525327231545Friday3297.00000013505.2857149.90285719.142857646.142857284.42857126.71428642.4285711851.428571